Robust gender classification using extended multi-spectral imaging by exploring the spectral angle mapper

Abstract

Gender classification based on the facial characteristic, has been widely studied in the literature across visible and near infrared spectrum. In this paper, we explore the applicability of extended multi-spectral imaging for the gender classification by quantifying the photometric property of the captured image. We proposed a novel scheme based on the Spectral Angle Mapper (SAM) that can effectively capture the spectral information across the multi-spectral bands that is further classified using the linear Support Vector Machine (SVM). Extensive set of experiments are carried out using a newly constructed multi-spectral face database with 78300 samples stemming from 145 subjects in six different scenarios. The obtained results show the best average classification accuracy of 93.51%, signifying the applicability of the proposed approach on the extended multi-spectral face data for robust gender classification.